Reduce to 100 examples for quick demo

train_qwen3_codeforces.py

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+# /// script
+# dependencies = [
+#     "trl>=0.12.0",
+#     "peft>=0.7.0",
+#     "transformers>=4.36.0",
+#     "accelerate>=0.24.0",
+#     "datasets>=2.14.0",
+#     "trackio",
+#     "torch",
+#     "bitsandbytes",
+# ]
+# ///
+
+import os
+import trackio
+from datasets import load_dataset
+from peft import LoraConfig
+from trl import SFTTrainer, SFTConfig
+from transformers import AutoTokenizer
+from huggingface_hub import login
+
+# Login with HF token
+hf_token = os.environ.get("HF_TOKEN")
+if hf_token:
+    login(token=hf_token)
+    print("Logged in to Hugging Face Hub")
+else:
+    print("Warning: HF_TOKEN not found in environment")
+
+# Load dataset - using the solutions configuration with messages format
+print("Loading open-r1/codeforces-cots dataset...")
+dataset = load_dataset("open-r1/codeforces-cots", "solutions", split="train")
+print(f"Full dataset loaded: {len(dataset)} examples")
+
+# Take 100 examples for quick demo
+dataset = dataset.select(range(min(100, len(dataset))))
+print(f"Using {len(dataset)} examples for demo training")
+
+# The dataset has both 'prompt' (string) and 'messages' (chat format) columns
+# TRL gets confused with both present. Keep only 'messages' for chat-based SFT.
+print("Preparing dataset for chat-based SFT...")
+
+# Filter for valid messages and keep only the messages column
+def filter_valid_messages(example):
+    """Filter out samples with empty or invalid messages"""
+    messages = example.get("messages", [])
+    if not messages or len(messages) < 2:
+        return False
+    for msg in messages:
+        if not msg.get("content"):
+            return False
+    return True
+
+dataset = dataset.filter(filter_valid_messages)
+print(f"After filtering: {len(dataset)} examples")
+
+# Remove all columns except 'messages' to avoid confusion
+columns_to_remove = [col for col in dataset.column_names if col != "messages"]
+dataset = dataset.remove_columns(columns_to_remove)
+print(f"Dataset columns: {dataset.column_names}")
+
+# Create train/eval split
+print("Creating train/eval split...")
+dataset_split = dataset.train_test_split(test_size=0.1, seed=42)
+train_dataset = dataset_split["train"]
+eval_dataset = dataset_split["test"]
+print(f"   Train: {len(train_dataset)} examples")
+print(f"   Eval: {len(eval_dataset)} examples")
+
+# Load tokenizer for chat template
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+# Training configuration
+config = SFTConfig(
+    # CRITICAL: Hub settings
+    output_dir="qwen3-0.6b-codeforces-sft",
+    push_to_hub=True,
+    hub_model_id="Godsonntungi2/qwen3-0.6b-codeforces-sft",
+    hub_strategy="every_save",
+    hub_token=hf_token,  # Explicitly pass token
+
+    # Training parameters
+    num_train_epochs=3,
+    per_device_train_batch_size=2,
+    per_device_eval_batch_size=1,  # Smaller eval batch to prevent OOM
+    gradient_accumulation_steps=8,
+    learning_rate=2e-5,
+    max_length=1024,  # Reduced from 2048 to save memory
+
+    # Logging & checkpointing
+    logging_steps=10,
+    save_strategy="steps",
+    save_steps=100,
+    save_total_limit=2,
+
+    # Evaluation - disable to save memory and time
+    eval_strategy="no",
+
+    # Optimization
+    warmup_ratio=0.1,
+    lr_scheduler_type="cosine",
+    gradient_checkpointing=True,
+    bf16=True,
+
+    # Monitoring
+    report_to="trackio",
+    project="qwen3-codeforces-sft",
+    run_name="demo-1k-v2",
+)
+
+# LoRA configuration for efficient training
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM",
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
+)
+
+# Initialize and train
+print("Initializing trainer with Qwen/Qwen3-0.6B...")
+trainer = SFTTrainer(
+    model="Qwen/Qwen3-0.6B",
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    processing_class=tokenizer,
+    args=config,
+    peft_config=peft_config,
+)
+
+print("Starting training...")
+trainer.train()
+
+print("Pushing to Hub...")
+trainer.push_to_hub()
+
+print("Complete! Model at: https://huggingface.co/Godsonntungi2/qwen3-0.6b-codeforces-sft")
+print("View metrics at: https://huggingface.co/spaces/Godsonntungi2/trackio")